Discovery of Frequent Patterns of Episodes Within a Time Window for Alarm Management Systems

Autor: Adel Hidri, Ahmed Selmi, Minyar Sassi Hidri
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: IEEE Access, Vol 8, Pp 11061-11073 (2020)
Druh dokumentu: article
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2020.2965647
Popis: The sequential pattern mining field is expanding through numerous researches and has a large number of applications such as language processing, alarms management and event management on a broader scale. Its use began with processing items baskets to learn patterns and have a directed marketing strategy but it is generalized to telecommunication alarms management with several works. Our work is in line with this, as it tries to locate patterns and identify them to make predictive statements about certain patterns. It is axed around providing a way to break sequences into episodes and assigning them a value of confidence and support, more precisely in the discovery of frequent patterns of episodes within a time window. Experimental results have shown the effectiveness of our sequential pattern mining approach and its adaptability to alarm management and analytics.
Databáze: Directory of Open Access Journals